Iris
Kuti K. Matyas
1/28/2017
Elsőként az Iris adathalmazt választottam. Programozási nyelvnek pedig az R-t azon belül ay H2O csomagot. Egy picit ‘overkill’ a feladathoz de hasznosnak találtam kipróbálni mivel munkában osztott rendszeren dolgozunk(Hadoop) és H2O-val lehet HDFS-ben tárolt nagyobb adathalmazokat is feldolgozni(összekötöttem a hasznost a hasznossal). Hátránya is van: kevés modell van implementálva. Például az SVM hiánzyik mivel nehezebben párhuzamositható osztott rendszeren.
Az iris adathalmaz kicsi viszont szerintem egész jól lehet majd mérni az osztályozók pontosságát mivel az egyik osztály elválasztható a másik 2től lineárisan, de az utóbbi 2 nem szétválasztható.(lásd alábbi ábra 3 változóval)
library(plotly)
plot_ly(iris,x=~Petal.Length,y=~Sepal.Length, z=~Petal.Width, color = ~Species, type="scatter3d",marker = list(opacity=0.5))Az adatok
Csupán 4 valós változó van a virágok szirmainak méréseivel valamint a virág tipusa. Előfeldolgozást sem igényel az adathalmaz. A tipus átalakitható “one hot encoding” modszerrel ha éppenséggel valamelyik mérést szeretnénk becsülni a többi paraméter alapján. Az adathalmaz az R környezet része, nem volt szükség letölteni.
library(h2o)
h2o.init(nthreads = 8) #kapcsolódni-elinditani a szervert##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## /tmp/Rtmp02gYGL/h2o_thecodewriter_started_from_r.out
## /tmp/Rtmp02gYGL/h2o_thecodewriter_started_from_r.err
##
##
## Starting H2O JVM and connecting: .. Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 2 seconds 613 milliseconds
## H2O cluster version: 3.10.3.3
## H2O cluster version age: 5 days
## H2O cluster name: H2O_started_from_R_thecodewriter_aih502
## H2O cluster total nodes: 1
## H2O cluster total memory: 26.67 GB
## H2O cluster total cores: 32
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## R Version: R version 3.3.2 (2016-10-31)irisdf <-as.h2o(iris) #betolteni az adatokat a 'szerverbe'##
|
| | 0%
|
|=================================================================| 100%summary(irisdf)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.1000
## 1st Qu.:5.099 1st Qu.:2.799 1st Qu.:1.596 1st Qu.:0.2992
## Median :5.798 Median :2.998 Median :4.348 Median :1.3000
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.1993
## 3rd Qu.:6.399 3rd Qu.:3.298 3rd Qu.:5.095 3rd Qu.:1.7992
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.5000
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## isplit<-h2o.splitFrame(irisdf,ratios = .8, destination_frames = c("train","test"),seed =121) #felosztas
itrain <- isplit[[1]]
itest <- isplit[[2]]
summary(itrain)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.1000
## 1st Qu.:5.099 1st Qu.:2.799 1st Qu.:1.496 1st Qu.:0.2992
## Median :5.799 Median :2.998 Median :4.248 Median :1.3000
## Mean :5.838 Mean :3.078 Mean :3.708 Mean :1.1700
## 3rd Qu.:6.398 3rd Qu.:3.324 3rd Qu.:5.095 3rd Qu.:1.7992
## Max. :7.700 Max. :4.400 Max. :6.900 Max. :2.5000
## Species
## setosa :42
## versicolor:39
## virginica :39
##
##
## summary(itest)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.500 Min. :2.200 Min. :1.300 Min. :0.2000
## 1st Qu.:5.398 1st Qu.:2.724 1st Qu.:2.196 1st Qu.:0.5479
## Median :5.897 Median :2.949 Median :4.498 Median :1.4489
## Mean :5.863 Mean :2.973 Mean :3.957 Mean :1.3167
## 3rd Qu.:6.299 3rd Qu.:3.200 3rd Qu.:4.972 3rd Qu.:1.7985
## Max. :7.900 Max. :3.900 Max. :6.400 Max. :2.5000
## Species
## versicolor:11
## virginica :11
## setosa : 8
##
##
## A split nem tökéletesen arányos mivel nagy adathalmazokra van kitalálva és arra megfeleloen mukodik.
Készitetek egy normalizált adathalmazt is az eredménzek összehasonlitása érdekében.
library(clusterSim) #normalizalashoz hasznalt csomag## Loading required package: cluster## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:plotly':
##
## select##
## This is package 'modeest' written by P. PONCET.
## For a complete list of functions, use 'library(help = "modeest")' or 'help.start()'.iris_norm <- iris
iris_norm$Sepal.Length <-data.Normalization(iris_norm$Sepal.Length,type = 'n12') # n12 = ((x-mean)/sqrt(sum((x-mean)^2)))
iris_norm$Sepal.Width <-data.Normalization(iris_norm$Sepal.Width,type = 'n12')
iris_norm$Petal.Length <-data.Normalization(iris_norm$Petal.Length,type = 'n12')
iris_norm$Petal.Width <-data.Normalization(iris_norm$Petal.Width,type = 'n12')
irisnormdf <-as.h2o(iris_norm)##
|
| | 0%
|
|=================================================================| 100%isplit<-h2o.splitFrame(irisnormdf,ratios = .8, destination_frames = c("train","test"),seed =121) #felosztas
intrain <- isplit[[1]]
intest <- isplit[[2]]
summary(intrain)## Warning in summary.H2OFrame(intrain): Approximated quantiles
## computed! If you are interested in exact quantiles, please pass the
## `exact_quantiles=TRUE` parameter.## Sepal.Length Sepal.Width Petal.Length
## Min. :-0.1526868 Min. :-0.198731 Min. :-0.127992
## 1st Qu.:-0.0736392 1st Qu.:-0.048517 1st Qu.:-0.104992
## Median :-0.0043465 Median :-0.011077 Median : 0.022737
## Mean :-0.0004947 Mean : 0.003947 Mean :-0.002305
## 3rd Qu.: 0.0548551 3rd Qu.: 0.050046 3rd Qu.: 0.062028
## Max. : 0.1836859 Max. : 0.252361 Max. : 0.145813
## Petal.Width Species
## Min. :-0.118153 setosa :42
## 1st Qu.:-0.096744 versicolor:39
## Median : 0.010819 virginica :39
## Mean :-0.003153
## 3rd Qu.: 0.064472
## Max. : 0.139792summary(intest)## Warning in summary.H2OFrame(intest): Approximated quantiles
## computed! If you are interested in exact quantiles, please pass the
## `exact_quantiles=TRUE` parameter.## Sepal.Length Sepal.Width Petal.Length
## Min. :-0.132900 Min. :-0.16114 Min. :-0.11407
## 1st Qu.:-0.044098 1st Qu.:-0.06257 1st Qu.:-0.07247
## Median : 0.005349 Median :-0.02039 Median : 0.03433
## Mean : 0.001979 Mean :-0.01579 Mean : 0.00922
## 3rd Qu.: 0.045041 3rd Qu.: 0.02674 3rd Qu.: 0.05634
## Max. : 0.203473 Max. : 0.15838 Max. : 0.12261
## Petal.Width Species
## Min. :-0.10741 versicolor:11
## 1st Qu.:-0.07002 virginica :11
## Median : 0.02682 setosa : 8
## Mean : 0.01261
## 3rd Qu.: 0.06440
## Max. : 0.13979plot_ly(iris_norm,x=~Petal.Length,y=~Sepal.Length, z=~Petal.Width, color = ~Species, type="scatter3d",marker = list(opacity=0.5))## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-modeAz osztályozók
A 3 osztályozó amit választottam: Naive Bayes, Neuralis háló és Random forest .
Bayes
bayes <- h2o.naiveBayes(x=1:4,y=5,itrain)##
|
| | 0%
|
|====================================================== | 83%
|
|=================================================================| 100%bayes@model$training_metrics## H2OMultinomialMetrics: naivebayes
## ** Reported on training data. **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("train")`
## MSE: (Extract with `h2o.mse`) 0.0220811
## RMSE: (Extract with `h2o.rmse`) 0.1485971
## Logloss: (Extract with `h2o.logloss`) 0.07090578
## Mean Per-Class Error: 0.03418803
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 42 0 0 0.0000 = 0 / 42
## versicolor 0 37 2 0.0513 = 2 / 39
## virginica 0 2 37 0.0513 = 2 / 39
## Totals 42 39 39 0.0333 = 4 / 120
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.966667
## 2 2 1.000000
## 3 3 1.000000bayes@model$apriori## A Priori Response Probabilities:
## setosa versicolor virginica
## 1 0.350000 0.325000 0.325000bayes@model$pcond## [[1]]
## Sepal.Length:
## y_by_sepallength mean std_dev
## 1 setosa -0.081785 0.035632
## 2 versicolor 0.005860 0.053590
## 3 virginica 0.080694 0.056365
##
## [[2]]
## Sepal.Width:
## y_by_sepalwidth mean std_dev
## 1 setosa 0.076489 0.065613
## 2 versicolor -0.058970 0.060644
## 3 virginica -0.011258 0.047331
##
## [[3]]
## Petal.Length:
## y_by_petallength mean std_dev
## 1 setosa -0.106446 0.008503
## 2 versicolor 0.021821 0.023518
## 3 virginica 0.085721 0.025013
##
## [[4]]
## Petal.Width:
## y_by_petalwidth mean std_dev
## 1 setosa -0.102544 0.011917
## 2 versicolor 0.011646 0.021122
## 3 virginica 0.089085 0.028855perfbayes <- h2o.performance(bayes,itest)
h2o.confusionMatrix(perfbayes)## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 8 0 0 0.0000 = 0 / 8
## versicolor 0 10 1 0.0909 = 1 / 11
## virginica 0 2 9 0.1818 = 2 / 11
## Totals 8 12 10 0.1000 = 3 / 30Neuralis halo
nn <- h2o.deeplearning(x=1:4,y=5,itrain,hidden = c(10),epochs = 1000,diagnostics=TRUE,variable_importances = TRUE,export_weights_and_biases=TRUE,standardize = FALSE) #egy 10es rejtett reteg##
|
| | 0%
|
|====== | 10%
|
|=================================================================| 100%nn@model$training_metrics## H2OMultinomialMetrics: deeplearning
## ** Reported on training data. **
## ** Metrics reported on full training frame **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("train")`
## MSE: (Extract with `h2o.mse`) 0.04500374
## RMSE: (Extract with `h2o.rmse`) 0.2121409
## Logloss: (Extract with `h2o.logloss`) 0.1460662
## Mean Per-Class Error: 0.05982906
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 42 0 0 0.0000 = 0 / 42
## versicolor 0 39 0 0.0000 = 0 / 39
## virginica 0 7 32 0.1795 = 7 / 39
## Totals 42 46 32 0.0583 = 7 / 120
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.941667
## 2 2 1.000000
## 3 3 1.000000nn@model$model_summary## Status of Neuron Layers: predicting Species, 3-class classification, multinomial distribution, CrossEntropy loss, 83 weights/biases, 4.0 KB, 120,000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate rate_rms
## 1 1 4 Input 0.00 %
## 2 2 10 Rectifier 0.00 % 0.000000 0.000000 0.022304 0.010972
## 3 3 3 Softmax 0.000000 0.000000 0.328551 0.452897
## momentum mean_weight weight_rms mean_bias bias_rms
## 1
## 2 0.000000 0.353523 2.347559 1.075514 0.880512
## 3 0.000000 -0.494950 3.088834 -0.023552 0.103008h2o.varimp_plot(nn)
h2o.weights(nn,matrix_id = 1)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 -0.09561024 -0.25414366 1.7936552 2.317148
## 2 -1.19130766 1.48091447 0.5580021 1.819052
## 3 1.01845717 0.06272632 -0.8774408 -2.536774
## 4 -0.37841806 2.10815573 -3.3898327 -3.984785
## 5 1.79173267 -0.74941695 2.3940663 4.084359
## 6 -0.88762665 0.50654703 2.2416735 2.467150
##
## [10 rows x 4 columns]h2o.biases(nn,vector_id = 1)## C1
## 1 -0.04846028
## 2 0.29538057
## 3 1.58846495
## 4 1.86967185
## 5 0.49847456
## 6 0.19380591
##
## [10 rows x 1 column]perfnn <- h2o.performance(nn,itest)
h2o.confusionMatrix(perfnn)## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 8 0 0 0.0000 = 0 / 8
## versicolor 0 11 0 0.0000 = 0 / 11
## virginica 0 3 8 0.2727 = 3 / 11
## Totals 8 14 8 0.1000 = 3 / 30Random forest
rf <- h2o.randomForest(x=1:4,y=5,itrain, ntrees = 40) #20 fabol allo RF##
|
| | 0%
|
|=================================================================| 100%rf@model$training_metrics## H2OMultinomialMetrics: drf
## ** Reported on training data. **
## ** Metrics reported on Out-Of-Bag training samples **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("train")`
## MSE: (Extract with `h2o.mse`) 0.01976119
## RMSE: (Extract with `h2o.rmse`) 0.1405745
## Logloss: (Extract with `h2o.logloss`) 0.06214647
## Mean Per-Class Error: 0.03418803
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 42 0 0 0.0000 = 0 / 42
## versicolor 0 37 2 0.0513 = 2 / 39
## virginica 0 2 37 0.0513 = 2 / 39
## Totals 42 39 39 0.0333 = 4 / 120
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.966667
## 2 2 1.000000
## 3 3 1.000000rf@model$model_summary## Model Summary:
## number_of_trees number_of_internal_trees model_size_in_bytes min_depth
## 1 40 120 18311 1
## max_depth mean_depth min_leaves max_leaves mean_leaves
## 1 6 3.01667 2 12 4.48333h2o.varimp_plot(rf)
perfrf <- h2o.performance(rf,itest)
h2o.confusionMatrix(perfrf)## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 8 0 0 0.0000 = 0 / 8
## versicolor 0 10 1 0.0909 = 1 / 11
## virginica 0 2 9 0.1818 = 2 / 11
## Totals 8 12 10 0.1000 = 3 / 30gendata <- function(n,params){
sl1 <- params[[1]][1,] #s-sepal,p-petal l-lenght w-width 1-setosa
sw1 <- params[[2]][1,]
pl1 <- params[[3]][1,]
pw1 <- params[[4]][1,]
dfsetosa<-cbind(rnorm(n=n,mean = sl1["mean"]$mean,sd = sl1["std_dev"]$std_dev),
rnorm(n=n,mean = sw1["mean"]$mean,sd = sw1["std_dev"]$std_dev),
rnorm(n=n,mean = pl1["mean"]$mean,sd = pl1["std_dev"]$std_dev),
rnorm(n=n,mean = pw1["mean"]$mean,sd = pw1["std_dev"]$std_dev),
rep("setosa",times=n))
dfsetosa <- as.data.frame(dfsetosa)
names(dfsetosa)<-c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species")
sl1 <- params[[1]][2,] #2-versicolor
sw1 <- params[[2]][2,]
pl1 <- params[[3]][2,]
pw1 <- params[[4]][2,]
dfversicolor<-cbind(rnorm(n=n,mean = sl1["mean"]$mean,sd = sl1["std_dev"]$std_dev),
rnorm(n=n,mean = sw1["mean"]$mean,sd = sw1["std_dev"]$std_dev),
rnorm(n=n,mean = pl1["mean"]$mean,sd = pl1["std_dev"]$std_dev),
rnorm(n=n,mean = pw1["mean"]$mean,sd = pw1["std_dev"]$std_dev),
rep("versicolor",times=n))
dfversicolor<- as.data.frame(dfversicolor)
names(dfversicolor)<-c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species")
sl1 <- params[[1]][3,] #3-virginica
sw1 <- params[[2]][3,]
pl1 <- params[[3]][3,]
pw1 <- params[[4]][3,]
dfvirginica<-cbind(rnorm(n=n,mean = sl1["mean"]$mean,sd = sl1["std_dev"]$std_dev),
rnorm(n=n,mean = sw1["mean"]$mean,sd = sw1["std_dev"]$std_dev),
rnorm(n=n,mean = pl1["mean"]$mean,sd = pl1["std_dev"]$std_dev),
rnorm(n=n,mean = pw1["mean"]$mean,sd = pw1["std_dev"]$std_dev),
rep("virginica",times=n))
dfvirginica<- as.data.frame(dfvirginica)
names(dfvirginica)<-c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species")
rbind(dfsetosa,dfversicolor,dfvirginica,stringsAsFactors=FALSE)
}
testd <- gendata(n=200,bayes@model$pcond)
testd$Sepal.Length <- as.double(as.character(testd$Sepal.Length))
testd$Petal.Length <- as.double(as.character(testd$Petal.Length))
testd$Sepal.Width <- as.double(as.character(testd$Sepal.Width))
testd$Petal.Width <- as.double(as.character(testd$Petal.Width))
plot_ly(testd,x=~Petal.Length,y=~Sepal.Length, z=~Petal.Width, color = ~Species,marker = list(opacity=0.5))## No trace type specified:
## Based on info supplied, a 'scatter3d' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter3d## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-modetd<-as.h2o(testd)##
|
| | 0%
|
|=================================================================| 100%h2o.performance(nn,td)## H2OMultinomialMetrics: deeplearning
##
## Test Set Metrics:
## =====================
##
## MSE: (Extract with `h2o.mse`) 0.04503741
## RMSE: (Extract with `h2o.rmse`) 0.2122202
## Logloss: (Extract with `h2o.logloss`) 0.1740993
## Mean Per-Class Error: 0.05833333
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>, <data>)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 200 0 0 0.0000 = 0 / 200
## versicolor 0 200 0 0.0000 = 0 / 200
## virginica 0 35 165 0.1750 = 35 / 200
## Totals 200 235 165 0.0583 = 35 / 600
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>, <data>)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.941667
## 2 2 1.000000
## 3 3 1.000000h2o.performance(bayes,td)## H2OMultinomialMetrics: naivebayes
##
## Test Set Metrics:
## =====================
##
## MSE: (Extract with `h2o.mse`) 0.006274718
## RMSE: (Extract with `h2o.rmse`) 0.07921311
## Logloss: (Extract with `h2o.logloss`) 0.02144053
## Mean Per-Class Error: 0.01
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>, <data>)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 200 0 0 0.0000 = 0 / 200
## versicolor 0 196 4 0.0200 = 4 / 200
## virginica 0 2 198 0.0100 = 2 / 200
## Totals 200 198 202 0.0100 = 6 / 600
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>, <data>)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.990000
## 2 2 1.000000
## 3 3 1.000000h2o.performance(rf,td)## H2OMultinomialMetrics: drf
##
## Test Set Metrics:
## =====================
##
## MSE: (Extract with `h2o.mse`) 0.02130447
## RMSE: (Extract with `h2o.rmse`) 0.1459605
## Logloss: (Extract with `h2o.logloss`) 0.08037285
## Mean Per-Class Error: 0.02333333
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>, <data>)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 200 0 0 0.0000 = 0 / 200
## versicolor 1 192 7 0.0400 = 8 / 200
## virginica 0 6 194 0.0300 = 6 / 200
## Totals 201 198 201 0.0233 = 14 / 600
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>, <data>)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.976667
## 2 2 1.000000
## 3 3 1.000000testd<-cbind(testd,as.data.frame(h2o.predict(nn,td)))##
|
| | 0%
|
|=================================================================| 100%testd$val <- ifelse(testd$Species == testd$predict,"+","-")
plot_ly(testd,x=~Petal.Length,y=~Sepal.Length, z=~Petal.Width, color = ~val,colors=c("red","green"),marker = list(opacity=0.2),text=~paste(Species," ",predict))## No trace type specified:
## Based on info supplied, a 'scatter3d' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter3d
## No scatter3d mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-modenn <- h2o.deeplearning(x=2:4,y=5,irisdf,hidden = c(10),epochs = 1000,diagnostics=TRUE,variable_importances = TRUE,export_weights_and_biases=TRUE) ##
|
| | 0%
|
|=================================================================| 100%nn@model$training_metrics## H2OMultinomialMetrics: deeplearning
## ** Reported on training data. **
## ** Metrics reported on full training frame **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("iris")`
## MSE: (Extract with `h2o.mse`) 0.01465887
## RMSE: (Extract with `h2o.rmse`) 0.1210738
## Logloss: (Extract with `h2o.logloss`) 0.04474672
## Mean Per-Class Error: 0.02666667
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 50 0 0 0.0000 = 0 / 50
## versicolor 0 47 3 0.0600 = 3 / 50
## virginica 0 1 49 0.0200 = 1 / 50
## Totals 50 48 52 0.0267 = 4 / 150
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.973333
## 2 2 1.000000
## 3 3 1.000000#nn@model$model_summary
h2o.varimp_plot(nn)
nn@model$variable_importances## Variable Importances:
## variable relative_importance scaled_importance percentage
## 1 Petal.Width 1.000000 1.000000 0.387752
## 2 Petal.Length 0.971460 0.971460 0.376685
## 3 Sepal.Width 0.607509 0.607509 0.235563rf <- h2o.randomForest(x=1:4,y=5,itrain, ntrees = 200) ##
|
| | 0%
|
|=== | 5%
|
|=================================================================| 100%rf@model$training_metrics## H2OMultinomialMetrics: drf
## ** Reported on training data. **
## ** Metrics reported on Out-Of-Bag training samples **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("train")`
## MSE: (Extract with `h2o.mse`) 0.02046987
## RMSE: (Extract with `h2o.rmse`) 0.143073
## Logloss: (Extract with `h2o.logloss`) 0.06656551
## Mean Per-Class Error: 0.03418803
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: vertical: actual; across: predicted
## setosa versicolor virginica Error Rate
## setosa 42 0 0 0.0000 = 0 / 42
## versicolor 0 37 2 0.0513 = 2 / 39
## virginica 0 2 37 0.0513 = 2 / 39
## Totals 42 39 39 0.0333 = 4 / 120
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-3 Hit Ratios:
## k hit_ratio
## 1 1 0.966667
## 2 2 1.000000
## 3 3 1.000000rf@model$model_summary## Model Summary:
## number_of_trees number_of_internal_trees model_size_in_bytes min_depth
## 1 200 600 92160 1
## max_depth mean_depth min_leaves max_leaves mean_leaves
## 1 9 3.14667 2 13 4.50333h2o.varimp_plot(rf)
x <- seq(0,8,by=0.1)
y <- seq(0,7,by=0.1)
z <- seq(0,3,by=0.1)
gr<-expand.grid(Sepal.Length=x,Petal.Length=y,Petal.Width=z)
grid<-as.h2o(gr,destination_frame = "grid")##
|
| | 0%
|
|=================================================================| 100%prrf<-as.data.frame(h2o.predict(rf,grid))##
|
| | 0%
|
|=================================================================| 100%prnn<-as.data.frame(h2o.predict(nn,grid))##
|
| | 0%
|
|=================================================================| 100%plot_ly(gr,x=~Petal.Length,y=~Sepal.Length, z=~Petal.Width, color = prrf$predict,marker = list(opacity=0.2))